Migration agent employing moveslice request

ABSTRACT

A migration agent, which is part of a distributed storage network, identifies one or more data objects stored as sets of encoded data slices in a first storage pool, and determines, for each of those data objects, whether to migrate corresponding sets of encoded data slices from the first storage pool to another storage pool. For at least one of the data objects, the migration agent determines to migrate a set of encoded data slices, and issues a set of MoveSlice requests to storage units included in the first storage pool, from which the data slices will be migrated. In response to the MoveSlice requests, the migration agent receives MoveSlice responses from the storage units in the first storage pool. If a threshold number of favorable MoveSlice responses is received, the migration agent facilitates deletion of the migrated encoded data slices from the first storage pool.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation-in-part of U.S. Utility applicationSer. No. 15/006,845, entitled “PRIORITIZING REBUILDING OF ENCODED DATASLICES” filed Jan. 26, 2016, which claims priority pursuant to 35 U.S.C.§ 119(e) to U.S. Provisional Application No. 62/141,034, entitled“REBUILDING ENCODED DATA SLICES ASSOCIATED WITH STORAGE ERRORS,” filedMar. 31, 2015, all of which are hereby incorporated herein by referencein their entirety and made part of the present U.S. Utility patentapplication for all purposes.

BACKGROUND Technical Field

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

In some conventional distributed storage systems, storage unitsresponsible for distributed storage of various data portions are taskedwith transferring data between each other as part of a dataredistribution or migration protocol. In some cases, however, decisionsregarding where to move the data do not take into account where otherdata, which may be related to the data being migrated, is stored. Whenmigrating data, failing to account for the locations where related datais stored can lead to migration of the data to less-than-ideal storageunits, potentially causing undesired geographic or logical datafragmentation, inefficient use of link resources, consistency errors,data conflicts with data portions already stored on the destinationstorage unit, or the like.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a decentralizedagreement module in accordance with the present invention;

FIG. 10 is a flowchart illustrating an example of selecting the resourcein accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 12 is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention; and

FIG. 14 is a flowchart illustrating an example of migrating encoded dataslices from a first storage pool to a second storage pool in accordancewith the present invention.

DETAILED DESCRIPTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 and 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data (e.g., data 40) as subsequently described withreference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generate aper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate a per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

Referring next to FIGS. 9-14, coordinated data migration using amigration agent will be discussed according to various embodiments ofthe present disclosure. When using a Distributed Agreement Protocol(DAP) to handle migration of data sources from one storage pool toanother, it is generally possible to arrange the additional distributedstorage (DS) units of the newly added storage pool such that they arerelatively local, and connected by fast/inexpensive links, to the DSunits which will be transferring data between each other as part of theredistribution of data according to the DAP. In some embodiments, tohelp reduce or minimize consistency errors, an attempt is made to ensurethat a fully readable and restorable source exists on an entire pool ofDS units, rather than some slices existing on one but not another. Toaccomplish this, all slices of a given source can be transferredtogether, as part of one managed operation, rather than individual DSunits deciding which slices to move and when to move them withoutconsideration of the related slices for that source on other DS units.In at least some embodiments, this coordination is provided by amigration agent.

The migration agent can determine, for a given source to be migrated, onwhich DS units those slices are held. To avoid the bottleneck andexpense involved with having the migration agent read these slices andthen write them to the DS units of the new storage pool, a new protocolmessage is introduced: the “MoveSlice” Request. This request is issuedby the Migration agent to its peers within its storage pool that holdslices of a source to be moved to a new storage pool. The requestindicates at least one slice name to be moved. In response the recipientds unit returns a MoveSlice Response, which indicates the revisions ofthat slice name that it holds, and the revisions that it was able tosuccessfully transfer to the appropriate ds unit in the new storagepool, as well as the identity of that storage pool. This determinationof which storage pool to move it to can be based on the DAP). If the dsunit determines there is no alternate pool it should be moved to, itreturns an error indictor. If it fails to transfer any revisions of theslice, it returns an empty list of “successfully moved slices” in theresponse. The migration agent considers, based on the responses from upto a width number of DS units, whether the latest restorable revisionwas successfully transferred to at least a write threshold number of DSunits on the same alternate storage pool. If it was successfullytransferred, then the migration agent will initiate a cleanup of thatslice on its own storage pool, e.g. via finalize/undo. The MoveSlicerequest allows a highly parallelized, local, and point-to-point transferof slices, greatly accelerating the speed of the migration whileminimizing cost.”

FIG. 9 is a schematic block diagram of an embodiment of a decentralizedagreement module 350 that includes a set of deterministic functions 1-N,a set of normalizing functions 1-N, a set of scoring functions 1-N, anda ranking function 352. Each of the deterministic function, thenormalizing function, the scoring function, and the ranking function352, may be implemented utilizing the computing core 26 of FIG. 2. Thedecentralized agreement module 350 may be implemented utilizing anymodule and/or unit of a dispersed storage network (DSN). For example,the decentralized agreement module can be implemented utilizingprocessing module 84, which can include the distributed storage (DS)client module 34 of FIG. 1, the computing core 26 of FIG. 2, or thelike.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSclient module 34 of FIG. 1, a distributed storage and task (DST)processing unit, such as computing device 16 of FIG. 1, an integrityprocessing unit 20 of FIG. 1, a managing unit 18 of FIG. 1, a userdevice such as computing devices 12 or 14 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, acyclic redundancy code function, hashing module of a number oflocations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 2 appends alocation ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function includes rank ordering each scorewith other scores of the set of scores 1-N, where a highest score isranked first. As such, a location associated with the highest score maybe considered a highest priority location for resource utilization(e.g., accessing, storing, retrieving, etc., the given asset of therequest). Having generated the ranked scoring information 358, thedecentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

FIG. 10 is a flowchart illustrating an example of selecting a resource.The method begins or continues at step 360 where a processing module(e.g., of a decentralized agreement module) receives a ranked scoringinformation request from a requesting entity with regards to a set ofcandidate resources. For each candidate resource, the method continuesat step 362 where the processing module performs a deterministicfunction on a location identifier (ID) of the candidate resource and anasset ID of the ranked scoring information request to produce an interimresult. As a specific example, the processing module combines the assetID and the location ID of the candidate resource to produce a combinedvalue and performs a hashing function on the combined value to producethe interim result.

For each interim result, the method continues at step 364 where theprocessing module performs a normalizing function on the interim resultto produce a normalized interim result. As a specific example, theprocessing module obtains a permutation value associated with thedeterministic function (e.g., maximum number of permutations of outputof the deterministic function) and divides the interim result by thepermutation value to produce the normalized interim result (e.g., with avalue between 0 and 1).

For each normalized interim result, the method continues at step 366where the processing module performs a scoring function on thenormalized interim result utilizing a location weight associated withthe candidate resource associated with the interim result to produce ascore of a set of scores. As a specific example, the processing moduledivides the location weight by a negative log of the normalized interimresult to produce the score.

The method continues at step 368 where the processing module rank ordersthe set of scores to produce ranked scoring information (e.g., ranking ahighest value first). The method continues at step 370 where theprocessing module outputs the ranked scoring information to therequesting entity. The requesting entity may utilize the ranked scoringinformation to select one location of a plurality of locations.

FIG. 11 is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the distributed storage (DST)processing unit 383, which can be implemented using computing device 16of FIG. 1, the network 24 of FIG. 1, and the distributed storage network(DSN) memory 22 of FIG. 1. Hereafter, the DSN memory 22 may beinterchangeably referred to as a DSN memory. The DST processing unit 383includes a decentralized agreement module 380 and processing module 84,which can be implemented using computing core 26 of FIG. 2. Thedecentralized agreement module 380 be implemented utilizing thedecentralized agreement module 350 of FIG. 9. The DSN memory 22 includesa plurality of DST execution (EX) unit pools 1-P. Each DST executionunit pool includes one or more sites 1-S. Each site includes one or moreDST execution units 1-N. Each DST execution unit may be associated withat least one pillar of N pillars associated with an informationdispersal algorithm (IDA), where a data segment is dispersed storageerror encoded using the IDA to produce one or more sets of encoded dataslices, and where each set includes N encoded data slices and likeencoded data slices (e.g., slice 3's) of two or more sets of encodeddata slices are included in a common pillar (e.g., pillar 3). Each sitemay not include every pillar and a given pillar may be implemented atmore than one site. Each DST execution unit includes a plurality ofmemories 1-M. Each DST execution unit may be implemented utilizing thestorage unit 36 of FIG. 1. Hereafter, a DST execution unit may bereferred to interchangeably as a storage unit and a set of DST executionunits may be interchangeably referred to as a set of storage unitsand/or as a storage unit set.

The DSN functions to receive data access requests 382, select resourcesof at least one DST execution unit pool for data access, utilize theselected DST execution unit pool for the data access, and issue a dataaccess response 392 based on the data access. The selecting of theresources includes utilizing a decentralized agreement function of thedecentralized agreement module 380, where a plurality of locations areranked against each other. The selecting may include selecting onestorage pool of the plurality of storage pools, selecting DST executionunits at various sites of the plurality of sites, selecting a memory ofthe plurality of memories for each DST execution unit, and selectingcombinations of memories, DST execution units, sites, pillars, andstorage pools.

In an example of operation, the processing module 84 receives the dataaccess request 382 from a requesting entity, where the data accessrequest 382 includes at least one of a store data request, a retrievedata request, a delete data request, a data name, and a requestingentity identifier (ID). Having received the data access request 382, theprocessing module 84 determines a DSN address associated with the dataaccess request. The DSN address includes at least one of a source name(e.g., including a vault ID and an object number associated with thedata name), a data segment ID, a set of slice names, a plurality of setsof slice names. The determining includes at least one of generating(e.g., for the store data request) and retrieving (e.g., from a DSNdirectory, from a dispersed hierarchical index) based on the data name(e.g., for the retrieve data request).

Having determined the DSN address, processing module 84 selects aplurality of resource levels (e.g., DST EX unit pool, site, DSTexecution unit, pillar, memory) associated with the DSN memory 22. Thedetermining may be based on one or more of the data name, the requestingentity ID, a predetermination, a lookup, a DSN performance indicator,and interpreting an error message. For example, the processing module 84selects the DST execution unit pool as a first resource level and a setof memory devices of a plurality of memory devices as a second resourcelevel based on a system registry lookup for a vault associated with therequesting entity.

Having selected the plurality of resource levels, the processing module84, for each resource level, issues a ranked scoring information request384 to the decentralized agreement module 380 utilizing the DSN addressas an asset ID. The decentralized agreement module 380 performs thedecentralized agreement function based on the asset ID (e.g., the DSNaddress), identifiers of locations of the selected resource levels, andlocation weights of the locations to generate ranked scoring information386.

For each resource level, the processing module 84 receives correspondingranked scoring information 386. Having received the ranked scoringinformation 386, the processing module 84 identifies one or moreresources associated with the resource level based on the rank scoringinformation 386. For example, the processing module 84 identifies a DSTexecution unit pool associated with a highest score and identifies a setof memory devices within DST execution units of the identified DSTexecution unit pool with a highest score.

Having identified the one or more resources, the processing module 84accesses the DSN memory 22 based on the identified one or more resourcesassociated with each resource level. For example, the processing module84 issues resource access requests 388 (e.g., write slice requests whenstoring data, read slice requests when recovering data) to theidentified DST execution unit pool, where the resource access requests388 further identify the identified set of memory devices. Havingaccessed the DSN memory 22, the processing module 84 receives resourceaccess responses 390 (e.g., write slice responses, read sliceresponses). The processing module 84 issues the data access response 392based on the received resource access responses 390. For example, theprocessing module 84 decodes received encoded data slices to reproducedata and generates the data access response 392 to include thereproduced data.

FIG. 12 is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory. The method begins or continues at step 394where a processing module (e.g., of a distributed storage and task (DST)client module) receives a data access request from a requesting entity.The data access request includes one or more of a storage request, aretrieval request, a requesting entity identifier, and a data identifier(ID). The method continues at step 396 where the processing moduledetermines a DSN address associated with the data access request. Forexample, the processing module generates the DSN address for the storagerequest. As another example, the processing module performs a lookup forthe retrieval request based on the data identifier.

The method continues at step 398 where the processing module selects aplurality of resource levels associated with the DSN memory. Theselecting may be based on one or more of a predetermination, a range ofweights associated with available resources, a resource performancelevel, and a resource performance requirement level. For each resourcelevel, the method continues at step 400 where the processing moduledetermines ranked scoring information. For example, the processingmodule issues a ranked scoring information request to a decentralizedagreement module based on the DSN address and receives correspondingranked scoring information for the resource level, where thedecentralized agreement module performs a decentralized agreementprotocol function on the DSN address using the associated resourceidentifiers and resource weights for the resource level to produce theranked scoring information for the resource level.

For each resource level, the method continues at step 402 where theprocessing module selects one or more resources associated with theresource level based on the ranked scoring information. For example, theprocessing module selects a resource associated with a highest scorewhen one resource is required. As another example, the processing moduleselects a plurality of resources associated with highest scores when aplurality of resources are required.

The method continues at step 404 where the processing module accessesthe DSN memory utilizing the selected one or more resources for each ofthe plurality of resource levels. For example, the processing moduleidentifies network addressing information based on the selectedresources including one or more of a storage unit Internet protocoladdress and a memory device identifier, generates a set of encoded dataslice access requests based on the data access request and the DSNaddress, and sends the set of encoded data slice access requests to theDSN memory utilizing the identified network addressing information.

The method continues at step 406 where the processing module issues adata access response to the requesting entity based on one or moreresource access responses from the DSN memory. For example, theprocessing module issues a data storage status indicator when storingdata. As another example, the processing module generates the dataaccess response to include recovered data when retrieving data.

FIG. 13 is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a migration agent 630, thenetwork 24 of FIG. 1, and a plurality of distributed storage and task(DST) execution (EX) unit pools 1-P. The migration agent 630 includes adecentralized agreement module 632 and a processing module 84implemented using computing core 26 of FIG. 2. The decentralizedagreement module 632 may be implemented utilizing the decentralizedagreement module 350 of FIG. 9. The migration agent 630 may beimplemented utilizing one or more of the computing device 16 of FIG. 1,the integrity processing unit 20 of FIG. 1, and the storage unit 36 ofFIG. 1. Each DST execution unit pool includes a set of DST executionunits 1-n. Each DST execution unit may be implemented utilizing thestorage unit 36 of FIG. 1.

The DSN functions to migrate encoded data slices from a first storagepool to a second storage pool, where one or more data objects are storedas sets of encoded data slices in at least one DST execution unit pool.For example, a data object A is stored as one or more sets of encodeddata slices A-1 through A-n in the DST execution units 1-n of the DSTexecution unit pool 1, a data object Z is stored as one or more sets ofencoded data slices Z-1 through Z-n in the DST execution units 1-n ofthe DST execution unit pool 1, and a data object W is stored as one ormore sets of encoded data slices W-1 through W-n in the DST executionunits 1-n of the DST execution unit pool 2.

In an example of operation of the migrating of the encoded data slices,the processing module 84 identifies one or more data objects stored assets of encoded data slices in the first DST execution unit pool. Theidentifying includes at least one of interpreting a DSN directory,interpreting entries of a dispersed hierarchical index, interpreting oneor more list slice responses, and interpreting a received request.

For each data object, the processing module 84 determines whether tomigrate the corresponding sets of encoded data slices to another storagepool. For example, the processing module 84, for each DST execution unitpool, issues a ranked scoring information request 634 to thedecentralized agreement module 632 where the request includes a DSNaddress of the data object, a location weight of the DST execution unitpool, and an identifier of the DST execution unit pool; receives rankedscoring information 636 in response; identifies a DST execution unitpool associated with a highest score of the ranked scoring information(e.g., a DST execution unit pool currently associated with the dataobject); and indicates to migrate when the identified DST execution unitpool is different than the first DST execution unit pool. As anotherexample, the processing module 84 indicates to migrate when detectingactivation of the other storage pool (e.g., activation of the DSTexecution unit pool 2). As yet another example, the processing module 84indicates to migrate when detecting that storage utilization of the DSTexecution unit pool 1 compares unfavorably to a storage utilizationthreshold level.

When migrating, for each identified data object, the processing module84 issues (e.g., generates and sends) a set of MoveSlice requests to theDST execution units of the DST execution unit pool 1, where the requestsincludes one or more of a slice name, a slice name range of associatedwith storage of the sets of encoded data slices of the data object,identity of corresponding DST execution units of the identified DSTexecution unit pool. For example, the processing module 84 sends, viathe network 24, MoveSlice requests A-1 through A-n to the DST executionunits 1-n of the DST execution unit pool 1.

Each DST execution unit receiving a MoveSlice request issues a writeslice request to a corresponding DST execution unit of the identifiedDST execution unit pool, where the write slice request includes encodeddata slice for migration. For example, the DST execution units 1-ninitiate sending of the encoded data slices A-1 through A-n via thenetwork 24 to the DST execution units 2. Alternatively, or in additionto, the DST execution unit of the DST execution unit pool 1 verifies thedestination of the slice migration by performing the distributedagreement protocol function to verify that the identified DST executionunit pool is associated with a highest score of re-ranked scoringinformation.

The processing module 84 facilitates deletion of the stored encoded dataslices of the move data object when receiving a threshold number offavorable MoveSlice responses from at least some DST execution units ofthe DST execution unit pool. For example, the processing module 84issues delete slice requests to the DST execution unit pool 1 withregards to the sets of encoded data slices A-1 through A-n whenreceiving a threshold number (e.g., a write threshold number) ofMoveSlice responses A-1 through A-n indicating success in moving theassociated encoded data slices. The processing module 84 may furtherupdate at least one of a DSN directory and a dispersed hierarchicalindex to disassociate slice names of the migrated encoded data slicesfrom the DST execution unit pool 1 and associate the slice names withthe DST execution unit pool 2 to facilitate subsequent retrieval of thedata object A from the DST execution unit pool 2 when not utilizing thedistributed agreement protocol function to identify the DST executionunit pool 2.

FIG. 14 is a flowchart illustrating an example of migrating encoded dataslices from a first storage pool to a second storage pool. The methodincludes step 640 where a processing module (e.g., of a distributedstorage and task (DST) client module, of a migration agent) identifiesone or more data objects stored as sets of encoded data slices in afirst storage pool of a dispersed storage network (DSN). The identifyingincludes at least one of interpreting a DSN directory, interpreting anentry of a dispersed hierarchical index, interpreting one or more listslice responses, and interpreting a received migration request.

For each data object, the method continues at step 642 where theprocessing module determines whether to migrate corresponding sets ofencoded data slices from the first storage pool to another storage pool.For example, for each storage pool, the processing module performs adistributed agreement protocol function on a DSN address of the dataobject utilizing a location weight of the storage pool to produce ascore of ranked scoring information for the storage pools, identifies anassociated storage pool based on the ranked scoring information (e.g.,highest score) and indicates to migrate when the identified storage poolis different than the first storage pool.

When migrating, the method continues at step 644, where, for each dataobject, the processing module issues a set of MoveSlice requests tostorage units of the first storage pool, where each MoveSlice requestidentifies a slice name of encoded data slice to be moved from the firststorage pool to the other storage pool. For example, the processingmodule generates the set of MoveSlice requests and sends the set ofMoveSlice requests to the storage units of the first storage pool, whereeach storage pool may perform the distributed agreement protocolfunction to verify identity of the other storage pool.

When receiving a threshold number of favorable MoveSlice responses fromat least some storage units of the first storage pool, the methodcontinues at step 646 where the processing module facilitates deletionof the corresponding sets of encoded data slices. For example, theprocessing module issues delete slice requests to the storage units ofthe first storage pool and may further update directory associations ofthe data object and the storage pools.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for use in a processing deviceconfigured to implement a migration agent, the migration agent operatingwithin a distributed storage network (DSN) configured to store dataobjects as sets of encoded data slices in a plurality of distributedstorage units organized as storage pools, the method comprising:identifying, at the migration agent, one or more data objects stored assets of encoded data slices in a first storage pool; for each dataobject of the one or more data objects, determining whether to migratecorresponding sets of encoded data slices from the first storage pool toanother storage pool; for at least one data object of the one or moredata objects: determining to migrate a set of encoded data slicesassociated with the at least one data object from the first storage poolto another storage pool; issuing a set of MoveSlice requests to storageunits included in the first storage pool; receiving, at the migrationagent, MoveSlice responses from at least some storage units included inthe first storage pool; and in response to receiving a threshold numberof favorable MoveSlice responses, facilitating deletion of thecorresponding sets of encoded data slices stored in the first storagepool.
 2. The method of claim 1, further comprising: determining whetherto migrate corresponding sets of encoded data slices by performing adistributed agreement protocol (DAP) function on a DSN address of the atleast one data object utilizing location weights of the storage pools toproduce ranked scoring information.
 3. The method of claim 2, furthercomprising: identifying an associated storage pool based on the rankedscoring information; and determining to migrate if the associatedstorage pool is different than the first storage pool.
 4. The method ofclaim 3, wherein: the DAP function is performed using a decentralizedagreement module included in the migration agent; and a MoveSlicerequest includes an identifier specifying the associated storage pool.5. The method of claim 3, wherein: the associated storage pool isidentified by performing the DAP function at each storage unit receivinga MoveSlice request.
 6. The method of claim 1, wherein: identifying theone or more data objects includes at least one of: interpreting a DSNdirectory, interpreting entries of a dispersed hierarchical index,interpreting one or more list slice responses, or interpreting areceived request.
 7. The method of claim 1, wherein determining whetherto migrate corresponding sets of encoded data slices from the firststorage pool to another storage pool includes: detecting a change in alocation weight of one or more storage pools.
 8. A migration agent,comprising: a processing module including a processor and associatedmemory; a decentralized agreement module coupled to the processingmodule; the processing module configured to: identify one or more dataobjects stored as sets of encoded data slices in a first storage pool ofa distributed storage network (DSN) configured to store data objects assets of encoded data slices in a plurality of distributed storage unitsorganized into storage pools; determine, for each data object of the oneor more data objects, whether to migrate corresponding sets of encodeddata slices from the first storage pool to another storage pool; for atleast one data object of the one or more data objects: determine tomigrate a set of encoded data slices associated with the at least onedata object from the first storage pool to another storage pool; issue aset of MoveSlice requests to storage units included in the first storagepool; receive, at the migration agent, MoveSlice responses from at leastsome storage units included in the first storage pool; and in responseto receiving a threshold number of favorable MoveSlice responses,facilitate deletion of the corresponding sets of encoded data slicesstored in the first storage pool.
 9. The migration agent of claim 8,wherein the processing module is further configured to: determinewhether to migrate corresponding sets of encoded data slices byperforming a distributed agreement protocol (DAP) function on a DSNaddress of the at least one data object utilizing location weights ofthe storage pools to produce ranked scoring information.
 10. Themigration agent of claim 9, the processing module configured to:identify an associated storage pool based on the ranked scoringinformation; and determine to migrate if the associated storage pool isdifferent than the first storage pool.
 11. The migration agent of claim10, wherein: the DAP function is performed using the decentralizedagreement module; and a MoveSlice request includes an identifierspecifying the associated storage pool.
 12. The migration agent of claim8, wherein: identifying the one or more data objects includes at leastone of: interpreting a DSN directory, interpreting entries of adispersed hierarchical index, interpreting one or more list sliceresponses, or interpreting a received request.
 13. The migration agentof claim 8, wherein determining whether to migrate corresponding sets ofencoded data slices from the first storage pool to another storage poolincludes: detecting a change in a location weight of one or more storagepools.
 14. A distributed storage network (DSN) comprising: a pluralityof distributed storage task execution (DST EX) units organized asstorage pools, each of the plurality of DST EX units including aplurality of distributed storage (DS) memories configured to store dataobjects as sets of encoded data slices; a migration agent coupled to theplurality of DST EX units via a communication network, the migrationagent including: a processing module including a processor andassociated memory; a decentralized agreement module coupled to theprocessing module; the processing module configured to: identify one ormore data objects stored as sets of encoded data slices in a firststorage pool; determine, for each data object of the one or more dataobjects, whether to migrate corresponding sets of encoded data slicesfrom the first storage pool to another storage pool; for at least onedata object of the one or more data objects: determine to migrate a setof encoded data slices associated with the at least one data object fromthe first storage pool to another storage pool; issue a set of MoveSlicerequests to storage units included in the first storage pool; receive,at the migration agent, MoveSlice responses from at least some storageunits included in the first storage pool; and in response to receiving athreshold number of favorable MoveSlice responses, facilitate deletionof the corresponding sets of encoded data slices stored in the firststorage pool.
 15. The distributed storage network (DSN) of claim 14, themigration agent further configured to: determine whether to migratecorresponding sets of encoded data slices by performing a distributedagreement protocol (DAP) function on a DSN address of the at least onedata object utilizing location weights of the storage pools to produceranked scoring information.
 16. The distributed storage network (DSN) ofclaim 15, the migration agent further configured to: identify anassociated storage pool based on the ranked scoring information; anddetermine to migrate if the associated storage pool is different thanthe first storage pool.
 17. The distributed storage network (DSN) ofclaim 16, wherein: the DAP function is performed using the decentralizedagreement module and the migration agent is configured to generate aMoveSlice request including an identifier specifying the associatedstorage pool.
 18. The distributed storage network (DSN) of claim 16,each of the plurality of DST EX units is further configured to: identifythe associated storage pool by performing the DAP function.
 19. Thedistributed storage network (DSN) of claim 14, the migration agentfurther configured to: identify the one or more data objects includes atleast one of: interpreting a DSN directory, interpreting entries of adispersed hierarchical index, interpreting one or more list sliceresponses, or interpreting a received request.
 20. The distributedstorage network (DSN) of claim 14, the migration agent furtherconfigured to: detect a change in a location weight of one or morestorage pools.